Overview

Dataset statistics

Number of variables15
Number of observations43555
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 MiB
Average record size in memory128.0 B

Variable types

Numeric11
Categorical2
Text2

Alerts

id is highly overall correlated with host_idHigh correlation
host_id is highly overall correlated with idHigh correlation
latitude is highly overall correlated with nbhd_groupHigh correlation
longitude is highly overall correlated with nbhd_groupHigh correlation
min_nights is highly overall correlated with reviews_count_ltmHigh correlation
reviews_count is highly overall correlated with monthly_reviews and 1 other fieldsHigh correlation
monthly_reviews is highly overall correlated with reviews_count and 1 other fieldsHigh correlation
reviews_count_ltm is highly overall correlated with min_nights and 2 other fieldsHigh correlation
nbhd_group is highly overall correlated with latitude and 1 other fieldsHigh correlation
id has unique valuesUnique
reviews_count has 10493 (24.1%) zerosZeros
monthly_reviews has 10493 (24.1%) zerosZeros
availability_365 has 13999 (32.1%) zerosZeros
reviews_count_ltm has 21644 (49.7%) zerosZeros

Reproduction

Analysis started2023-08-31 16:17:22.889545
Analysis finished2023-08-31 16:17:32.560190
Duration9.67 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43555
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5827836 × 1017
Minimum2595
Maximum9.0598181 × 1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size680.5 KiB
2023-08-31T12:17:32.609746image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2595
5-th percentile3262907.5
Q120220041
median45617541
Q36.798155 × 1017
95-th percentile8.6189966 × 1017
Maximum9.0598181 × 1017
Range9.0598181 × 1017
Interquartile range (IQR)6.798155 × 1017

Descriptive statistics

Standard deviation3.606055 × 1017
Coefficient of variation (CV)1.3961894
Kurtosis-1.3665879
Mean2.5827836 × 1017
Median Absolute Deviation (MAD)34621075
Skewness0.73106355
Sum-3.2000551 × 1018
Variance1.3003633 × 1035
MonotonicityNot monotonic
2023-08-31T12:17:32.680637image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5121 1
 
< 0.1%
5.599344622 × 10171
 
< 0.1%
5.615164303 × 10171
 
< 0.1%
5.643992905 × 10171
 
< 0.1%
5.615212336 × 10171
 
< 0.1%
5.644088355 × 10171
 
< 0.1%
5.61535863 × 10171
 
< 0.1%
5.644469911 × 10171
 
< 0.1%
5.598813841 × 10171
 
< 0.1%
5.615808741 × 10171
 
< 0.1%
Other values (43545) 43545
> 99.9%
ValueCountFrequency (%)
2595 1
< 0.1%
5121 1
< 0.1%
5136 1
< 0.1%
5178 1
< 0.1%
5203 1
< 0.1%
5803 1
< 0.1%
6848 1
< 0.1%
6872 1
< 0.1%
6990 1
< 0.1%
7064 1
< 0.1%
ValueCountFrequency (%)
9.059818095 × 10171
< 0.1%
9.059749213 × 10171
< 0.1%
9.059714426 × 10171
< 0.1%
9.05955885 × 10171
< 0.1%
9.059263206 × 10171
< 0.1%
9.05856981 × 10171
< 0.1%
9.058210671 × 10171
< 0.1%
9.056946472 × 10171
< 0.1%
9.056570747 × 10171
< 0.1%
9.056075412 × 10171
< 0.1%

host_id
Real number (ℝ)

HIGH CORRELATION 

Distinct27393
Distinct (%)62.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5593631 × 108
Minimum1678
Maximum5.1802193 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size680.5 KiB
2023-08-31T12:17:32.750413image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1678
5-th percentile1410227.1
Q116477260
median78464432
Q32.789252 × 108
95-th percentile4.8305642 × 108
Maximum5.1802193 × 108
Range5.1802026 × 108
Interquartile range (IQR)2.6244794 × 108

Descriptive statistics

Standard deviation1.6557727 × 108
Coefficient of variation (CV)1.0618263
Kurtosis-0.75610799
Mean1.5593631 × 108
Median Absolute Deviation (MAD)74767926
Skewness0.83525796
Sum6.791806 × 1012
Variance2.7415834 × 1016
MonotonicityNot monotonic
2023-08-31T12:17:32.823699image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107434423 569
 
1.3%
3223938 487
 
1.1%
305240193 412
 
0.9%
496944100 342
 
0.8%
19303369 235
 
0.5%
200239515 219
 
0.5%
204704622 207
 
0.5%
162280872 176
 
0.4%
137358866 142
 
0.3%
501999278 138
 
0.3%
Other values (27383) 40628
93.3%
ValueCountFrequency (%)
1678 1
 
< 0.1%
2234 1
 
< 0.1%
2438 1
 
< 0.1%
2571 1
 
< 0.1%
2782 1
 
< 0.1%
2787 7
< 0.1%
2845 3
< 0.1%
2868 1
 
< 0.1%
3757 1
 
< 0.1%
3869 1
 
< 0.1%
ValueCountFrequency (%)
518021933 1
< 0.1%
517950291 1
< 0.1%
517874537 1
< 0.1%
517704969 1
< 0.1%
517701802 1
< 0.1%
517613113 1
< 0.1%
517575835 1
< 0.1%
517575039 1
< 0.1%
517427494 1
< 0.1%
517408696 1
< 0.1%

nbhd_group
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size680.5 KiB
Manhattan
18101 
Brooklyn
16341 
Queens
6971 
Bronx
 
1710
Staten Island
 
432

Length

Max length13
Median length9
Mean length8.0272988
Min length5

Characters and Unicode

Total characters349629
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrooklyn
2nd rowManhattan
3rd rowManhattan
4th rowBrooklyn
5th rowManhattan

Common Values

ValueCountFrequency (%)
Manhattan 18101
41.6%
Brooklyn 16341
37.5%
Queens 6971
 
16.0%
Bronx 1710
 
3.9%
Staten Island 432
 
1.0%

Length

2023-08-31T12:17:32.895620image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-31T12:17:32.977106image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
manhattan 18101
41.2%
brooklyn 16341
37.1%
queens 6971
 
15.8%
bronx 1710
 
3.9%
staten 432
 
1.0%
island 432
 
1.0%

Most occurring characters

ValueCountFrequency (%)
n 62088
17.8%
a 55167
15.8%
t 37066
10.6%
o 34392
9.8%
M 18101
 
5.2%
h 18101
 
5.2%
B 18051
 
5.2%
r 18051
 
5.2%
l 16773
 
4.8%
y 16341
 
4.7%
Other values (10) 55498
15.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 305210
87.3%
Uppercase Letter 43987
 
12.6%
Space Separator 432
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 62088
20.3%
a 55167
18.1%
t 37066
12.1%
o 34392
11.3%
h 18101
 
5.9%
r 18051
 
5.9%
l 16773
 
5.5%
y 16341
 
5.4%
k 16341
 
5.4%
e 14374
 
4.7%
Other values (4) 16516
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
M 18101
41.2%
B 18051
41.0%
Q 6971
 
15.8%
S 432
 
1.0%
I 432
 
1.0%
Space Separator
ValueCountFrequency (%)
432
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 349197
99.9%
Common 432
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 62088
17.8%
a 55167
15.8%
t 37066
10.6%
o 34392
9.8%
M 18101
 
5.2%
h 18101
 
5.2%
B 18051
 
5.2%
r 18051
 
5.2%
l 16773
 
4.8%
y 16341
 
4.7%
Other values (9) 55066
15.8%
Common
ValueCountFrequency (%)
432
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 349629
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 62088
17.8%
a 55167
15.8%
t 37066
10.6%
o 34392
9.8%
M 18101
 
5.2%
h 18101
 
5.2%
B 18051
 
5.2%
r 18051
 
5.2%
l 16773
 
4.8%
y 16341
 
4.7%
Other values (10) 55498
15.9%

nbhd
Text

Distinct223
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size680.5 KiB
2023-08-31T12:17:33.094342image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length25
Median length17
Mean length11.739915
Min length4

Characters and Unicode

Total characters511332
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowBedford-Stuyvesant
2nd rowMidtown
3rd rowLower East Side
4th rowSunset Park
5th rowChinatown
ValueCountFrequency (%)
east 5589
 
8.0%
side 3884
 
5.5%
bedford-stuyvesant 3175
 
4.5%
upper 3151
 
4.5%
heights 2983
 
4.2%
harlem 2935
 
4.2%
williamsburg 2588
 
3.7%
west 2151
 
3.1%
village 2140
 
3.0%
midtown 2079
 
3.0%
Other values (236) 39608
56.4%
2023-08-31T12:17:33.285041image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 46205
 
9.0%
i 35991
 
7.0%
s 34826
 
6.8%
t 34741
 
6.8%
a 33502
 
6.6%
r 29514
 
5.8%
l 28221
 
5.5%
26728
 
5.2%
n 23793
 
4.7%
o 22584
 
4.4%
Other values (44) 195227
38.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 404719
79.1%
Uppercase Letter 74390
 
14.5%
Space Separator 26728
 
5.2%
Dash Punctuation 3633
 
0.7%
Other Punctuation 1862
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 46205
11.4%
i 35991
 
8.9%
s 34826
 
8.6%
t 34741
 
8.6%
a 33502
 
8.3%
r 29514
 
7.3%
l 28221
 
7.0%
n 23793
 
5.9%
o 22584
 
5.6%
d 18838
 
4.7%
Other values (15) 96504
23.8%
Uppercase Letter
ValueCountFrequency (%)
H 10070
13.5%
S 9944
13.4%
B 7264
9.8%
E 6354
 
8.5%
W 6274
 
8.4%
C 4842
 
6.5%
M 3540
 
4.8%
F 3271
 
4.4%
U 3204
 
4.3%
G 2983
 
4.0%
Other values (14) 16644
22.4%
Other Punctuation
ValueCountFrequency (%)
' 1650
88.6%
. 211
 
11.3%
, 1
 
0.1%
Space Separator
ValueCountFrequency (%)
26728
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3633
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 479109
93.7%
Common 32223
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 46205
 
9.6%
i 35991
 
7.5%
s 34826
 
7.3%
t 34741
 
7.3%
a 33502
 
7.0%
r 29514
 
6.2%
l 28221
 
5.9%
n 23793
 
5.0%
o 22584
 
4.7%
d 18838
 
3.9%
Other values (39) 170894
35.7%
Common
ValueCountFrequency (%)
26728
82.9%
- 3633
 
11.3%
' 1650
 
5.1%
. 211
 
0.7%
, 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 511332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 46205
 
9.0%
i 35991
 
7.0%
s 34826
 
6.8%
t 34741
 
6.8%
a 33502
 
6.6%
r 29514
 
5.8%
l 28221
 
5.5%
26728
 
5.2%
n 23793
 
4.7%
o 22584
 
4.4%
Other values (44) 195227
38.2%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct24586
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.72867
Minimum40.500314
Maximum40.91138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size680.5 KiB
2023-08-31T12:17:33.373478image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum40.500314
5-th percentile40.640904
Q140.68765
median40.72452
Q340.762735
95-th percentile40.829473
Maximum40.91138
Range0.41106557
Interquartile range (IQR)0.075085

Descriptive statistics

Standard deviation0.057559263
Coefficient of variation (CV)0.001413237
Kurtosis0.14019738
Mean40.72867
Median Absolute Deviation (MAD)0.037495659
Skewness0.21202247
Sum1773937.2
Variance0.0033130687
MonotonicityNot monotonic
2023-08-31T12:17:33.445540image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.76153 46
 
0.1%
40.76411 31
 
0.1%
40.7607614 22
 
0.1%
40.761532 22
 
0.1%
40.73756 19
 
< 0.1%
40.75436 19
 
< 0.1%
40.71899 18
 
< 0.1%
40.71579 18
 
< 0.1%
40.74926 17
 
< 0.1%
40.71933 17
 
< 0.1%
Other values (24576) 43326
99.5%
ValueCountFrequency (%)
40.50031443 1
< 0.1%
40.50456 1
< 0.1%
40.50848895 1
< 0.1%
40.50863 1
< 0.1%
40.51706 1
< 0.1%
40.52034 1
< 0.1%
40.52224 1
< 0.1%
40.52339 1
< 0.1%
40.52498 1
< 0.1%
40.52715449 1
< 0.1%
ValueCountFrequency (%)
40.91138 1
< 0.1%
40.91114684 1
< 0.1%
40.9111047 1
< 0.1%
40.91074 1
< 0.1%
40.91062 1
< 0.1%
40.91037273 1
< 0.1%
40.90884 1
< 0.1%
40.906568 1
< 0.1%
40.90530196 1
< 0.1%
40.90505 1
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct22105
Distinct (%)50.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.943913
Minimum-74.24984
Maximum-73.71087
Zeros0
Zeros (%)0.0%
Negative43555
Negative (%)100.0%
Memory size680.5 KiB
2023-08-31T12:17:33.517719image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-74.24984
5-th percentile-74.003651
Q1-73.98199
median-73.95272
Q3-73.924145
95-th percentile-73.820704
Maximum-73.71087
Range0.53897
Interquartile range (IQR)0.057845

Descriptive statistics

Standard deviation0.056333974
Coefficient of variation (CV)-0.00076184735
Kurtosis3.053246
Mean-73.943913
Median Absolute Deviation (MAD)0.02903
Skewness1.1441545
Sum-3220627.1
Variance0.0031735166
MonotonicityNot monotonic
2023-08-31T12:17:33.595583image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.99878 46
 
0.1%
-73.99371 29
 
0.1%
-73.99391 25
 
0.1%
-74.00587 23
 
0.1%
-73.97643 23
 
0.1%
-73.998779 22
 
0.1%
-73.9861055 22
 
0.1%
-73.9535 20
 
< 0.1%
-73.99773 17
 
< 0.1%
-73.98532 15
 
< 0.1%
Other values (22095) 43313
99.4%
ValueCountFrequency (%)
-74.24984 1
< 0.1%
-74.24308594 1
< 0.1%
-74.24135 1
< 0.1%
-74.23913565 1
< 0.1%
-74.21514 1
< 0.1%
-74.21126 1
< 0.1%
-74.21088 1
< 0.1%
-74.21065716 1
< 0.1%
-74.20739 1
< 0.1%
-74.20674 1
< 0.1%
ValueCountFrequency (%)
-73.71087 1
< 0.1%
-73.71299 1
< 0.1%
-73.71383551 1
< 0.1%
-73.7154939 1
< 0.1%
-73.7174099 1
< 0.1%
-73.71974787 1
< 0.1%
-73.71991 1
< 0.1%
-73.72054 1
< 0.1%
-73.7216024 1
< 0.1%
-73.72408 1
< 0.1%

room_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size680.5 KiB
Entire home/apt
24645 
Private room
18182 
Shared room
 
579
Hotel room
 
149

Length

Max length15
Median length15
Mean length13.677373
Min length10

Characters and Unicode

Total characters595718
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowEntire home/apt
3rd rowPrivate room
4th rowEntire home/apt
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt 24645
56.6%
Private room 18182
41.7%
Shared room 579
 
1.3%
Hotel room 149
 
0.3%

Length

2023-08-31T12:17:33.664349image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-31T12:17:33.732343image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
entire 24645
28.3%
home/apt 24645
28.3%
room 18910
21.7%
private 18182
20.9%
shared 579
 
0.7%
hotel 149
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 68200
11.4%
t 67621
11.4%
o 62614
10.5%
r 62316
10.5%
m 43555
 
7.3%
43555
 
7.3%
a 43406
 
7.3%
i 42827
 
7.2%
h 25224
 
4.2%
p 24645
 
4.1%
Other values (9) 111755
18.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 483963
81.2%
Space Separator 43555
 
7.3%
Uppercase Letter 43555
 
7.3%
Other Punctuation 24645
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 68200
14.1%
t 67621
14.0%
o 62614
12.9%
r 62316
12.9%
m 43555
9.0%
a 43406
9.0%
i 42827
8.8%
h 25224
 
5.2%
p 24645
 
5.1%
n 24645
 
5.1%
Other values (3) 18910
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
E 24645
56.6%
P 18182
41.7%
S 579
 
1.3%
H 149
 
0.3%
Space Separator
ValueCountFrequency (%)
43555
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 24645
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 527518
88.6%
Common 68200
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 68200
12.9%
t 67621
12.8%
o 62614
11.9%
r 62316
11.8%
m 43555
8.3%
a 43406
8.2%
i 42827
8.1%
h 25224
 
4.8%
p 24645
 
4.7%
E 24645
 
4.7%
Other values (7) 62465
11.8%
Common
ValueCountFrequency (%)
43555
63.9%
/ 24645
36.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 595718
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 68200
11.4%
t 67621
11.4%
o 62614
10.5%
r 62316
10.5%
m 43555
 
7.3%
43555
 
7.3%
a 43406
 
7.3%
i 42827
 
7.2%
h 25224
 
4.2%
p 24645
 
4.1%
Other values (9) 111755
18.8%

price
Real number (ℝ)

Distinct1235
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218.86385
Minimum10
Maximum20500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size680.5 KiB
2023-08-31T12:17:33.799747image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile42
Q180
median136
Q3229
95-th percentile560
Maximum20500
Range20490
Interquartile range (IQR)149

Descriptive statistics

Standard deviation442.46204
Coefficient of variation (CV)2.0216314
Kurtosis433.71325
Mean218.86385
Median Absolute Deviation (MAD)64
Skewness16.618359
Sum9532615
Variance195772.65
MonotonicityNot monotonic
2023-08-31T12:17:33.867284image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 1171
 
2.7%
100 1029
 
2.4%
200 928
 
2.1%
80 717
 
1.6%
50 714
 
1.6%
75 692
 
1.6%
120 690
 
1.6%
60 681
 
1.6%
70 658
 
1.5%
250 655
 
1.5%
Other values (1225) 35620
81.8%
ValueCountFrequency (%)
10 6
< 0.1%
15 2
 
< 0.1%
16 3
 
< 0.1%
18 2
 
< 0.1%
19 4
 
< 0.1%
20 13
< 0.1%
21 4
 
< 0.1%
22 12
< 0.1%
23 14
< 0.1%
24 10
< 0.1%
ValueCountFrequency (%)
20500 1
 
< 0.1%
19750 1
 
< 0.1%
15000 1
 
< 0.1%
10000 26
0.1%
9999 4
 
< 0.1%
9994 1
 
< 0.1%
9990 1
 
< 0.1%
9397 1
 
< 0.1%
9000 1
 
< 0.1%
8000 2
 
< 0.1%

min_nights
Real number (ℝ)

HIGH CORRELATION 

Distinct127
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.579474
Minimum1
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size680.5 KiB
2023-08-31T12:17:33.934467image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median15
Q330
95-th percentile30
Maximum500
Range499
Interquartile range (IQR)28

Descriptive statistics

Standard deviation25.378979
Coefficient of variation (CV)1.3659686
Kurtosis92.297952
Mean18.579474
Median Absolute Deviation (MAD)14
Skewness7.3440779
Sum809229
Variance644.09257
MonotonicityNot monotonic
2023-08-31T12:17:34.152253image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 19094
43.8%
1 7976
18.3%
2 5143
 
11.8%
3 3746
 
8.6%
5 1515
 
3.5%
4 1410
 
3.2%
7 907
 
2.1%
31 840
 
1.9%
90 476
 
1.1%
6 375
 
0.9%
Other values (117) 2073
 
4.8%
ValueCountFrequency (%)
1 7976
18.3%
2 5143
11.8%
3 3746
8.6%
4 1410
 
3.2%
5 1515
 
3.5%
6 375
 
0.9%
7 907
 
2.1%
8 60
 
0.1%
9 29
 
0.1%
10 224
 
0.5%
ValueCountFrequency (%)
500 6
 
< 0.1%
480 1
 
< 0.1%
400 4
 
< 0.1%
370 1
 
< 0.1%
367 1
 
< 0.1%
366 1
 
< 0.1%
365 53
0.1%
364 3
 
< 0.1%
360 10
 
< 0.1%
356 1
 
< 0.1%

reviews_count
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct490
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.373367
Minimum0
Maximum2024
Zeros10493
Zeros (%)24.1%
Negative0
Negative (%)0.0%
Memory size680.5 KiB
2023-08-31T12:17:34.228623image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q324
95-th percentile131
Maximum2024
Range2024
Interquartile range (IQR)23

Descriptive statistics

Standard deviation57.746498
Coefficient of variation (CV)2.1895763
Kurtosis89.245198
Mean26.373367
Median Absolute Deviation (MAD)5
Skewness6.1295728
Sum1148692
Variance3334.6581
MonotonicityNot monotonic
2023-08-31T12:17:34.305539image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10493
24.1%
1 4235
 
9.7%
2 2719
 
6.2%
3 2035
 
4.7%
4 1602
 
3.7%
5 1345
 
3.1%
6 1155
 
2.7%
7 1002
 
2.3%
8 832
 
1.9%
9 786
 
1.8%
Other values (480) 17351
39.8%
ValueCountFrequency (%)
0 10493
24.1%
1 4235
9.7%
2 2719
 
6.2%
3 2035
 
4.7%
4 1602
 
3.7%
5 1345
 
3.1%
6 1155
 
2.7%
7 1002
 
2.3%
8 832
 
1.9%
9 786
 
1.8%
ValueCountFrequency (%)
2024 1
< 0.1%
1734 1
< 0.1%
1380 1
< 0.1%
1131 1
< 0.1%
1069 1
< 0.1%
1052 1
< 0.1%
1025 1
< 0.1%
958 1
< 0.1%
770 1
< 0.1%
763 1
< 0.1%
Distinct2859
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size680.5 KiB
2023-08-31T12:17:34.450665image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length11
Median length10
Mean length10.240914
Min length10

Characters and Unicode

Total characters446043
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique549 ?
Unique (%)1.3%

Sample

1st row2019-12-02
2nd row2022-06-21
3rd row2023-05-14
4th row2022-08-10
5th row2023-04-28
ValueCountFrequency (%)
unavailable 10493
 
24.1%
2023-05-21 1250
 
2.9%
2023-05-29 1112
 
2.6%
2023-05-28 865
 
2.0%
2023-05-20 692
 
1.6%
2023-05-22 674
 
1.5%
2023-05-30 561
 
1.3%
2023-05-31 535
 
1.2%
2023-05-19 500
 
1.1%
2023-06-04 449
 
1.0%
Other values (2849) 26424
60.7%
2023-08-31T12:17:34.673724image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 82592
18.5%
0 77699
17.4%
- 66124
14.8%
1 31858
 
7.1%
a 31479
 
7.1%
3 26393
 
5.9%
l 20986
 
4.7%
5 15709
 
3.5%
n 10493
 
2.4%
U 10493
 
2.4%
Other values (9) 72217
16.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 264496
59.3%
Lowercase Letter 104930
 
23.5%
Dash Punctuation 66124
 
14.8%
Uppercase Letter 10493
 
2.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 82592
31.2%
0 77699
29.4%
1 31858
 
12.0%
3 26393
 
10.0%
5 15709
 
5.9%
9 7129
 
2.7%
6 6222
 
2.4%
8 6176
 
2.3%
4 5786
 
2.2%
7 4932
 
1.9%
Lowercase Letter
ValueCountFrequency (%)
a 31479
30.0%
l 20986
20.0%
n 10493
 
10.0%
e 10493
 
10.0%
b 10493
 
10.0%
i 10493
 
10.0%
v 10493
 
10.0%
Dash Punctuation
ValueCountFrequency (%)
- 66124
100.0%
Uppercase Letter
ValueCountFrequency (%)
U 10493
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 330620
74.1%
Latin 115423
 
25.9%

Most frequent character per script

Common
ValueCountFrequency (%)
2 82592
25.0%
0 77699
23.5%
- 66124
20.0%
1 31858
 
9.6%
3 26393
 
8.0%
5 15709
 
4.8%
9 7129
 
2.2%
6 6222
 
1.9%
8 6176
 
1.9%
4 5786
 
1.8%
Latin
ValueCountFrequency (%)
a 31479
27.3%
l 20986
18.2%
n 10493
 
9.1%
U 10493
 
9.1%
e 10493
 
9.1%
b 10493
 
9.1%
i 10493
 
9.1%
v 10493
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 446043
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 82592
18.5%
0 77699
17.4%
- 66124
14.8%
1 31858
 
7.1%
a 31479
 
7.1%
3 26393
 
5.9%
l 20986
 
4.7%
5 15709
 
3.5%
n 10493
 
2.4%
U 10493
 
2.4%
Other values (9) 72217
16.2%

monthly_reviews
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct880
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.92609069
Minimum0
Maximum63.95
Zeros10493
Zeros (%)24.1%
Negative0
Negative (%)0.0%
Memory size680.5 KiB
2023-08-31T12:17:34.770703image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.23
Q31.27
95-th percentile3.93
Maximum63.95
Range63.95
Interquartile range (IQR)1.26

Descriptive statistics

Standard deviation1.6339397
Coefficient of variation (CV)1.7643409
Kurtosis210.55088
Mean0.92609069
Median Absolute Deviation (MAD)0.23
Skewness8.3931511
Sum40335.88
Variance2.669759
MonotonicityNot monotonic
2023-08-31T12:17:34.841335image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10493
 
24.1%
0.02 1166
 
2.7%
0.01 1015
 
2.3%
0.04 790
 
1.8%
0.03 777
 
1.8%
0.05 671
 
1.5%
0.07 617
 
1.4%
1 551
 
1.3%
0.06 544
 
1.2%
0.09 515
 
1.2%
Other values (870) 26416
60.6%
ValueCountFrequency (%)
0 10493
24.1%
0.01 1015
 
2.3%
0.02 1166
 
2.7%
0.03 777
 
1.8%
0.04 790
 
1.8%
0.05 671
 
1.5%
0.06 544
 
1.2%
0.07 617
 
1.4%
0.08 470
 
1.1%
0.09 515
 
1.2%
ValueCountFrequency (%)
63.95 1
< 0.1%
61.15 1
< 0.1%
55.95 1
< 0.1%
51.48 1
< 0.1%
51.41 1
< 0.1%
41.61 1
< 0.1%
40.05 1
< 0.1%
38.84 1
< 0.1%
35.95 1
< 0.1%
32.37 1
< 0.1%

host_list_count
Real number (ℝ)

Distinct70
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.941958
Minimum1
Maximum569
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size680.5 KiB
2023-08-31T12:17:34.915415image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile219
Maximum569
Range568
Interquartile range (IQR)4

Descriptive statistics

Standard deviation99.120115
Coefficient of variation (CV)3.2034209
Kurtosis16.44208
Mean30.941958
Median Absolute Deviation (MAD)0
Skewness4.0821216
Sum1347677
Variance9824.7973
MonotonicityNot monotonic
2023-08-31T12:17:34.983403image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 22514
51.7%
2 5613
 
12.9%
3 2786
 
6.4%
4 1715
 
3.9%
5 920
 
2.1%
6 750
 
1.7%
8 584
 
1.3%
569 569
 
1.3%
7 553
 
1.3%
487 487
 
1.1%
Other values (60) 7064
 
16.2%
ValueCountFrequency (%)
1 22514
51.7%
2 5613
 
12.9%
3 2786
 
6.4%
4 1715
 
3.9%
5 920
 
2.1%
6 750
 
1.7%
7 553
 
1.3%
8 584
 
1.3%
9 378
 
0.9%
10 280
 
0.6%
ValueCountFrequency (%)
569 569
1.3%
487 487
1.1%
412 412
0.9%
342 342
0.8%
235 235
0.5%
219 219
 
0.5%
207 207
 
0.5%
176 176
 
0.4%
142 142
 
0.3%
138 138
 
0.3%

availability_365
Real number (ℝ)

ZEROS 

Distinct366
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.00241
Minimum0
Maximum365
Zeros13999
Zeros (%)32.1%
Negative0
Negative (%)0.0%
Memory size680.5 KiB
2023-08-31T12:17:35.056914image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median89
Q3278
95-th percentile362.3
Maximum365
Range365
Interquartile range (IQR)278

Descriptive statistics

Standard deviation137.34309
Coefficient of variation (CV)1.0024866
Kurtosis-1.4234077
Mean137.00241
Median Absolute Deviation (MAD)89
Skewness0.43091324
Sum5967140
Variance18863.124
MonotonicityNot monotonic
2023-08-31T12:17:35.129794image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13999
32.1%
365 1267
 
2.9%
364 668
 
1.5%
339 323
 
0.7%
181 318
 
0.7%
358 249
 
0.6%
363 243
 
0.6%
155 231
 
0.5%
1 222
 
0.5%
308 220
 
0.5%
Other values (356) 25815
59.3%
ValueCountFrequency (%)
0 13999
32.1%
1 222
 
0.5%
2 166
 
0.4%
3 155
 
0.4%
4 155
 
0.4%
5 106
 
0.2%
6 78
 
0.2%
7 103
 
0.2%
8 109
 
0.3%
9 75
 
0.2%
ValueCountFrequency (%)
365 1267
2.9%
364 668
1.5%
363 243
 
0.6%
362 191
 
0.4%
361 124
 
0.3%
360 164
 
0.4%
359 164
 
0.4%
358 249
 
0.6%
357 125
 
0.3%
356 111
 
0.3%

reviews_count_ltm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct155
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8839398
Minimum0
Maximum1128
Zeros21644
Zeros (%)49.7%
Negative0
Negative (%)0.0%
Memory size680.5 KiB
2023-08-31T12:17:35.206082image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38
95-th percentile40
Maximum1128
Range1128
Interquartile range (IQR)8

Descriptive statistics

Standard deviation18.252904
Coefficient of variation (CV)2.3152008
Kurtosis633.65043
Mean7.8839398
Median Absolute Deviation (MAD)1
Skewness15.116428
Sum343385
Variance333.16849
MonotonicityNot monotonic
2023-08-31T12:17:35.277791image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21644
49.7%
1 3234
 
7.4%
2 2139
 
4.9%
3 1674
 
3.8%
4 1288
 
3.0%
5 985
 
2.3%
6 812
 
1.9%
7 649
 
1.5%
8 552
 
1.3%
9 506
 
1.2%
Other values (145) 10072
23.1%
ValueCountFrequency (%)
0 21644
49.7%
1 3234
 
7.4%
2 2139
 
4.9%
3 1674
 
3.8%
4 1288
 
3.0%
5 985
 
2.3%
6 812
 
1.9%
7 649
 
1.5%
8 552
 
1.3%
9 506
 
1.2%
ValueCountFrequency (%)
1128 1
< 0.1%
954 1
< 0.1%
631 1
< 0.1%
578 1
< 0.1%
553 1
< 0.1%
542 1
< 0.1%
528 1
< 0.1%
475 1
< 0.1%
457 1
< 0.1%
375 1
< 0.1%

Interactions

2023-08-31T12:17:31.579236image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:24.417372image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:25.200291image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:25.949541image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:26.610127image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:27.291282image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:28.042030image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:28.708053image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:29.418991image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:30.100524image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:30.888587image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:31.637528image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:24.527576image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:25.343208image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:26.006820image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:26.667839image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:27.347858image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:28.100866image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:28.769081image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:29.478512image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:30.157502image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:30.949273image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:31.696682image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:24.634004image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:25.402399image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:26.066734image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:26.729073image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:27.406629image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:28.159825image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:28.833163image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:29.540568image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:30.217969image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:31.011082image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:31.755984image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:24.718058image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:25.461789image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:26.124883image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:26.788772image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:27.464483image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:28.219975image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:28.900719image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:29.599950image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:30.275133image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:31.072439image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:31.818007image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:24.782737image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:25.523293image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:26.185163image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:26.850478image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:27.523916image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:28.281682image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:28.966814image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:29.662861image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:30.337333image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:31.136748image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:31.874297image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:24.837409image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:25.581167image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:26.242773image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:26.907950image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:27.679061image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:28.339784image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:29.026976image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:29.721667image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:30.396232image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:31.197087image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:31.934300image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:24.896570image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:25.640574image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:26.300611image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:26.969509image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:27.737992image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:28.396959image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:29.090695image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:29.782593image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:30.456146image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:31.258518image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:32.002174image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:24.959493image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:25.705024image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:26.366497image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:27.040048image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:27.802597image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:28.463012image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:29.159099image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:29.849420image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:30.642793image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:31.325911image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:32.065764image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:25.020929image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:25.766230image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:26.427976image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:27.104423image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:27.863276image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:28.524319image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:29.224749image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:29.911327image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:30.705305image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:31.389430image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:32.124547image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:25.080155image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:25.825636image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:26.487586image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:27.164693image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:27.920735image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:28.583931image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:29.287642image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:29.973863image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:30.763860image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:31.452428image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:32.189625image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:25.143261image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:25.889919image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:26.551076image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:27.230669image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:27.984290image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:28.648235image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:29.355085image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:30.037617image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:30.828538image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-31T12:17:31.518184image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-08-31T12:17:35.344722image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
idhost_idlatitudelongitudepricemin_nightsreviews_countmonthly_reviewshost_list_countavailability_365reviews_count_ltmnbhd_grouproom_type
id1.0000.5340.0140.0900.155-0.345-0.3070.0680.3370.4120.1750.0570.031
host_id0.5341.0000.0580.1190.065-0.308-0.0790.1270.2540.2830.1690.1140.095
latitude0.0140.0581.0000.0070.0930.060-0.095-0.1010.102-0.007-0.0940.5570.081
longitude0.0900.1190.0071.000-0.401-0.1390.1170.170-0.0160.1270.1620.6770.108
price0.1550.0650.093-0.4011.000-0.186-0.0060.0420.0170.1830.0980.0320.011
min_nights-0.345-0.3080.060-0.139-0.1861.000-0.329-0.494-0.026-0.230-0.5270.0250.026
reviews_count-0.307-0.079-0.0950.117-0.006-0.3291.0000.866-0.1430.0820.7090.0140.022
monthly_reviews0.0680.127-0.1010.1700.042-0.4940.8661.000-0.0670.2200.8580.0280.033
host_list_count0.3370.2540.102-0.0160.017-0.026-0.143-0.0671.0000.3790.0340.1650.123
availability_3650.4120.283-0.0070.1270.183-0.2300.0820.2200.3791.0000.3470.0920.085
reviews_count_ltm0.1750.169-0.0940.1620.098-0.5270.7090.8580.0340.3471.0000.0100.000
nbhd_group0.0570.1140.5570.6770.0320.0250.0140.0280.1650.0920.0101.0000.093
room_type0.0310.0950.0810.1080.0110.0260.0220.0330.1230.0850.0000.0931.000

Missing values

2023-08-31T12:17:32.290685image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-31T12:17:32.452506image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idhost_idnbhd_groupnbhdlatitudelongituderoom_typepricemin_nightsreviews_countlast_reviewmonthly_reviewshost_list_countavailability_365reviews_count_ltm
051217356BrooklynBedford-Stuyvesant40.685350-73.955120Private room6030502019-12-020.29200
125952845ManhattanMidtown40.753560-73.985590Entire home/apt24030492022-06-210.3033251
21499159023ManhattanLower East Side40.722070-73.989760Private room1205222023-05-140.1412683
351367378BrooklynSunset Park40.662650-73.994540Entire home/apt2762132022-08-100.0312751
459709186084ManhattanChinatown40.717800-73.993200Entire home/apt3257692023-04-280.4713823
551788967ManhattanMidtown40.764570-73.983170Private room6825892023-05-143.4411051
61534160049ManhattanLower East Side40.721359-73.993668Entire home/apt31530352023-05-230.3011604
752037490ManhattanUpper West Side40.803800-73.967510Private room7521182017-07-210.71100
858039744BrooklynSouth Slope40.668010-73.987840Private room13432332023-05-151.36314821
960164289653ManhattanTribeca40.720120-74.003970Entire home/apt500301102022-11-180.771452
idhost_idnbhd_groupnbhdlatitudelongituderoom_typepricemin_nightsreviews_countlast_reviewmonthly_reviewshost_list_countavailability_365reviews_count_ltm
4355690501069565598038440319757ManhattanHell's Kitchen40.766500-73.988536Entire home/apt140030Unavailable0.0143620
4355790501360074218332537583303ManhattanFinancial District40.705111-74.011629Entire home/apt350300Unavailable0.021800
4355890501405630900195640319757ManhattanCivic Center40.714259-74.005310Entire home/apt99710Unavailable0.0143650
4355990501644963263850413384735BrooklynBushwick40.699660-73.922629Private room60300Unavailable0.023650
4356090501925919872932340319757ManhattanChinatown40.718730-74.001030Entire home/apt130010Unavailable0.0143640
4356190503107314986176086368678BrooklynCanarsie40.633458-73.899472Entire home/apt20830Unavailable0.033580
4356290503836246798859152064945BrooklynPark Slope40.677256-73.981435Private room530300Unavailable0.012700
4356390506655960729525822541573ManhattanEast Village40.732530-73.989880Entire home/apt382300Unavailable0.01322880
43564905077900484113845151692758BrooklynCanarsie40.638210-73.915920Entire home/apt19920Unavailable0.011550
4356590510935455278638025138314ManhattanMidtown40.765086-73.976717Private room66210Unavailable0.01162500